基于最小均值 (LMF) 和最小均方 (LMS) 算法进行系统识别附matlab代码.zip

%% System Identification Using Least Mean Forth (LMF) and Least Mean Square (LMS) algorithm % Plant identification simulation % title={FLMF: Fractional least mean fourth algorithm for channel estimation in non-Gaussian environment}, % author={Shujaat Khan and Naveed Ahmed and Muhammad Ammar Malik and Imran Naseem and Roberto Togneri and Mohammed Bennamoun}, % journal={2017 International Conference on Information and Communication Technology Convergence (ICTC)}, % year={2017}, % pages={466-470} % } % *Start* clc; clear all; close all; N = 1e4; % Number of samples Bits = 2; % For PSK modulation SNR = 10; % Noise level % *Monte Carlo simulations* % h = [0.9 0.2 0.5 -0.7]; % Plant1 impulse response % h = [-2:1:2]; % Plant2 impulse response h = randn(1,5); % Random system runs=100; NWDs = 0; NWDf = 0; temp3 = 0; temp4 = 0; for run = 1:runs % Monte Carlo simulations % h = randn(1,5); data = randi([0 (2^Bits)-1],1,N); % Random index for input data x = real(pskmod(data,2^Bits)); % Phase shit keying (PSK) modulation r = filter(h,1,x); % Input passed trought system(h) d = awun(r, SNR); % Addition of white Uniform noise of decined SNR % d = awgn(r, SNR); % Addition of white Gaussian noise of decined SNR % *LMS parameter* etas = 1e-2; % Learning rate for LMS Wlms = zeros(size(h)); % Initial weights of LMS Us = zeros(1,length(h)); % Input frame length of LMS % *LMF parameter* etaf = 1e-2; % Learning rate for LMF Wlmf = zeros(size(h)); % Initial weights of LMF Uf = zeros(1,length(h)); % Input frame length of LMF for n = 1 : N % *LMS* Us(1,2:end) = Us(1,1:end-1); % Shifting of frame window Us(1,1) = x(n); % Input of LMS ys = (Wlms)*Us'; % Output of LMS es = d(n) - ys; % Instantaneous error of LMS Wlms = Wlms + etas * es * Us; % Weight update rule of LMS % *LMF* Uf(1,2:end) = Uf(1,1:end-1); % Shifting of frame window Uf(1,1) = x(n); % Input of LMF yf = (Wlmf)*Uf'; % Output of LMF ef = d(n) - yf; % Instantaneous error of LMF Wlmf = Wlmf + etaf * (ef.^3) * Uf; % Weight update rule of LMF % *Normalized weight difference (NWD)* temp1(n) = norm(Wlms-h)./norm(h); % Normalized weight difference of LMS temp2(n) = norm(Wlmf-h)./norm(h); % Normalized weight difference of LMF end % *Accumulating Results* NWDs = NWDs + temp1; NWDf = NWDf + temp2; temp3 = temp3 + Wlms; temp4 = temp4 + Wlmf; end %% Average Results for (# of runs) of independent trials NWDs = NWDs./runs; NWDf = NWDf./runs; Wlms = temp3./runs; Wlmf = temp4./runs; %% Cost function plots figure fsize=14; % plot text font size plot(10*log10(NWDs),'','linewidth',4) hold on plot(10*log10(NWDf),'r','linewidth',4) lgh=legend(strcat('Least mean square (LMS):', int2str(SNR),' (dB)'),strcat('Least mean forth (LMF):', int2str(SNR),' (dB)'),'Location','NorthEast'); grid minor xlabel('Iterations','FontName','Times New Roman','FontSize',fsize); ylabel('Normalized weight difference (NWD) in (dB)','FontName','Times New Roman','FontSize',fsize); title('Cost function (NWD vs epochs iteration)','FontName','Times New Roman','FontSize',6*fsize/5); set(lgh,'FontName','Times New Roman','FontSize',fsize) set(gca,'FontName','Times New Roman','FontSize',fsize) saveas(gcf,strcat('LMS_LMF_Comparision.png'),'png') [h;Wlms;Wlmf]